I have a dataframe with test results (10k rows). Each result (row) has ~6 parameters, plus some output metrics. I would like to find combinations of the parameters which have reasonable mean, std and support-count (number of results in the configuration).

E.g. if there are parameters "k" and "n", each in range(100), and the result metric has good mean and std for "k in [4..12] and c in [90..95]" (support-count for this would be 8*5 = 40) and then maybe "k in [34..41] and c in [10..13] (s-c is 7*3=21), then I would like to have the algorithm return sth like

    k       c       mean   std    support-count total_score
    4..12   90..95  12.1   1.23   40            9.3
    34..41  10..13  11.1   1.13   21            6.2

I understand I will first have to define a fucntion that will reduce the mean, std and count to the total_score. I can do that somehow. But I don't know what kind of math task is finding the local maxima of parameter configuration subsets.

Is this optimization task? Can you please point me to sth in sklearn or scipy, that would give me some direction?


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